Python continues to dominate the programming world in 2025, thanks to its versatility, readability, and extensive ecosystem of libraries. Whether you're aiming for a role at a FAANG company (Google, Amazon, Facebook/Meta, Apple, Netflix) or an innovative startup, mastering Python is a must for software developers, data scientists, and engineers.
1. What is Python, and why is it popular?
Answer: Python is a high-level, interpreted, general-purpose programming language known for its simple syntax and readability. It supports multiple paradigms like object-oriented and functional programming. Its popularity stems from its ease of use, vast library support, and applications in diverse fields like AI, web development, and automation.
Asked by: TCS, Infosys, Wipro (common in fresher interviews).
2. What are Python’s built-in data types?
Answer: Python has several built-in data types, including:
3. What are the key differences between lists and tuples in Python?
Answer:
Asked by: Accenture, Cognizant (tests data structure basics).
4. Explain the difference between == and is in Python.
Answer: == checks for value equality (e.g., 5 == 5), while is checks for identity (whether two objects are the same in memory, e.g., a is b).
Asked by: Google (entry-level coding screens), Amazon (data engineer roles).
5. What is the difference between append() and extend() for lists?
Answer: append() adds a single element to the end of a list, while extend() adds each element of an iterable (e.g., list, tuple) individually to the list.
Example:
Asked by: Wipro, Accenture (data structure fundamentals).
6. How does Python handle memory management?
Answer: Python uses automatic memory management with a private heap space. The Python Memory Manager allocates memory, and the garbage collector (using reference counting) frees up memory when objects are no longer referenced.
Asked by: Microsoft (systems-related roles), IBM (data engineering).
7. What are decorators in Python, and how do they work?
Answer: Decorators are functions that modify the behavior of another function or method. They use the @decorator_name syntax and wrap the original function, enhancing or altering its functionality.
Example:
Asked by: Spotify (backend roles), Meta (software engineering).
8. Write a Python function to reverse a string.
Answer:
Asked by: Amazon (coding rounds), Netflix (data manipulation tasks).
9. How do you handle exceptions in Python?
Answer: Python uses try, except, else, and finally blocks to handle exceptions.
Example:
Answer: Lambda functions are small, anonymous functions defined with the lambda keyword. They’re used for short, one-off operations, often with functions like map(), filter(), or sorted().
Example:
Answer:
Answer: A shallow copy (copy.copy()) creates a new object but references nested objects, while a deep copy (copy.deepcopy()) creates a fully independent duplicate, including all nested objects.
Example:
Answer: Use generators instead of lists to save memory, leverage libraries like NumPy or Pandas for efficient computation, and employ multiprocessing for parallel execution. Avoid unnecessary loops with list comprehensions or built-in functions like map().
Asked by: Netflix (data engineering), Amazon (big data roles).
9. Explain the Global Interpreter Lock (GIL) and its impact.
Answer: The GIL is a mutex in CPython that prevents multiple native threads from executing Python bytecodes simultaneously. It simplifies memory management but limits true multi-threading performance, making multiprocessing a better choice for CPU-bound tasks.
Asked by: Meta (performance optimization), Microsoft (advanced Python roles).
17. What is a context manager, and how does the with statement work?
Answer: A context manager handles resource management (e.g., opening/closing files) using the with statement. It ensures resources are properly acquired and released, even if an error occurs. Custom context managers can be created using __enter__ and __exit__ methods or the @contextmanager decorator from contextlib.
Example:
Asked by: Meta (system design), Netflix (file handling in data pipelines).
18. How would you implement a LRU (Least Recently Used) cache in Python?
Answer: Use collections.OrderedDict or the built-in @functools.lru_cache decorator. Here’s a manual implementation:
19. Explain Python’s *args and **kwargs and provide an example.
Answer: *args allows a function to accept a variable number of positional arguments as a tuple, while **kwargs accepts a variable number of keyword arguments as a dictionary.
Example:
10. Find the first non-repeating character in a string.
Answer:
11. Merge two sorted lists into one sorted list.
Answer:
20. Write a Python function to find the maximum subarray sum (Kadane’s Algorithm).
Answer:
21. Flatten a nested list in Python.
Answer:
Have a favorite Python question or company-specific tip? Share in the comments below!
In this blog, we’ll explore the top Python interview questions for 2025, categorized by difficulty, and highlight companies known to ask them based on their tech stacks and hiring trends. Let’s dive in!
Why Python in 2025?
Python’s popularity isn’t slowing down. Its applications span web development (Django, Flask), data science (Pandas, NumPy), machine learning (TensorFlow, PyTorch), and automation (Selenium, subprocess). Companies like Google, Amazon, Netflix, Spotify, and Microsoft rely heavily on Python for their scalable systems, data pipelines, and AI-driven solutions. As of 2025, Python’s role in cloud computing and AI continues to grow, making it a hot skill for technical interviews.Beginner-Level Python Interview Questions
These questions test your foundational knowledge and are commonly asked in entry-level interviews at companies like TCS, Infosys, Accenture, and startups.1. What is Python, and why is it popular?
Answer: Python is a high-level, interpreted, general-purpose programming language known for its simple syntax and readability. It supports multiple paradigms like object-oriented and functional programming. Its popularity stems from its ease of use, vast library support, and applications in diverse fields like AI, web development, and automation.
Asked by: TCS, Infosys, Wipro (common in fresher interviews).
2. What are Python’s built-in data types?
Answer: Python has several built-in data types, including:
- Numeric: int, float, complex
- Sequence: list, tuple, str
- Mapping: dict
- Set: set, frozenset
- Boolean: bool
- None: NoneType
3. What are the key differences between lists and tuples in Python?
Answer:
- Lists are mutable (can be modified), defined with square brackets [], while tuples are immutable (cannot be changed), defined with parentheses ().
- Lists are slower than tuples due to mutability overhead.
Asked by: Accenture, Cognizant (tests data structure basics).
4. Explain the difference between == and is in Python.
Answer: == checks for value equality (e.g., 5 == 5), while is checks for identity (whether two objects are the same in memory, e.g., a is b).
Asked by: Google (entry-level coding screens), Amazon (data engineer roles).
5. What is the difference between append() and extend() for lists?
Answer: append() adds a single element to the end of a list, while extend() adds each element of an iterable (e.g., list, tuple) individually to the list.
Example:
lst = [1, 2]
lst.append([3, 4]) # [1, 2, [3, 4]]
lst = [1, 2]
lst.extend([3, 4]) # [1, 2, 3, 4]Asked by: Wipro, Accenture (data structure fundamentals).
Intermediate-Level Python Interview Questions
These questions assess your practical coding skills and are popular at companies like Microsoft, Spotify, and IBM.6. How does Python handle memory management?
Answer: Python uses automatic memory management with a private heap space. The Python Memory Manager allocates memory, and the garbage collector (using reference counting) frees up memory when objects are no longer referenced.
Asked by: Microsoft (systems-related roles), IBM (data engineering).
7. What are decorators in Python, and how do they work?
Answer: Decorators are functions that modify the behavior of another function or method. They use the @decorator_name syntax and wrap the original function, enhancing or altering its functionality.
Example:
def my_decorator(func):
def wrapper():
print("Before function call")
func()
print("After function call")
return wrapper
@my_decorator
def say_hello():
print("Hello!")
say_hello()
# Output: Before function call, Hello!, After function callAsked by: Spotify (backend roles), Meta (software engineering).
8. Write a Python function to reverse a string.
Answer:
def reverse_string(s):
return s[::-1] # Slicing with step -1 reverses the string
# Test
print(reverse_string("Python")) # Output: nohtyPAsked by: Amazon (coding rounds), Netflix (data manipulation tasks).
9. How do you handle exceptions in Python?
Answer: Python uses try, except, else, and finally blocks to handle exceptions.
Example:
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
else:
print("Division successful.")
finally:
print("Execution complete.")
# Output: Cannot divide by zero! Execution complete.
Asked by: Microsoft (error handling in scripts), IBM (automation roles).
15. What are lambda functions, and when should you use them?
15. What are lambda functions, and when should you use them?
Answer: Lambda functions are small, anonymous functions defined with the lambda keyword. They’re used for short, one-off operations, often with functions like map(), filter(), or sorted().
Example:
# Sort list of tuples by second element
data = [(1, 'b'), (2, 'a')]
sorted_data = sorted(data, key=lambda x: x[1])
print(sorted_data) # [(2, 'a'), (1, 'b')]
Asked by: Spotify (data processing), Amazon (functional programming).
16. Write a Python function to check if a number is a palindrome.
16. Write a Python function to check if a number is a palindrome.
Answer:
def is_palindrome(num):
return str(num) == str(num)[::-1]
# Test
print(is_palindrome(121)) # True
print(is_palindrome(123)) # False
Asked by: Google (basic string manipulation), TCS (coding rounds).
7. What is the difference between shallow and deep copy?
Advanced-Level Python Interview Questions
These questions target deeper understanding and problem-solving, often seen at Google, Meta, and Apple.7. What is the difference between shallow and deep copy?
Answer: A shallow copy (copy.copy()) creates a new object but references nested objects, while a deep copy (copy.deepcopy()) creates a fully independent duplicate, including all nested objects.
Example:
import copy
lst = [[1, 2], [3, 4]]
shallow = copy.copy(lst)
deep = copy.deepcopy(lst)
lst[0][0] = 9
print(shallow) # [[9, 2], [3, 4]]
print(deep) # [[1, 2], [3, 4]]Asked by: Google (algorithm design), Apple (systems programming).
8. How would you optimize a Python script handling large datasets?
8. How would you optimize a Python script handling large datasets?
Answer: Use generators instead of lists to save memory, leverage libraries like NumPy or Pandas for efficient computation, and employ multiprocessing for parallel execution. Avoid unnecessary loops with list comprehensions or built-in functions like map().
Asked by: Netflix (data engineering), Amazon (big data roles).
9. Explain the Global Interpreter Lock (GIL) and its impact.
Answer: The GIL is a mutex in CPython that prevents multiple native threads from executing Python bytecodes simultaneously. It simplifies memory management but limits true multi-threading performance, making multiprocessing a better choice for CPU-bound tasks.
Asked by: Meta (performance optimization), Microsoft (advanced Python roles).
17. What is a context manager, and how does the with statement work?
Answer: A context manager handles resource management (e.g., opening/closing files) using the with statement. It ensures resources are properly acquired and released, even if an error occurs. Custom context managers can be created using __enter__ and __exit__ methods or the @contextmanager decorator from contextlib.
Example:
with open('file.txt', 'r') as f:
content = f.read() # File is automatically closed after this blockAsked by: Meta (system design), Netflix (file handling in data pipelines).
18. How would you implement a LRU (Least Recently Used) cache in Python?
Answer: Use collections.OrderedDict or the built-in @functools.lru_cache decorator. Here’s a manual implementation:
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity):
self.capacity = capacity
self.cache = OrderedDict()
def get(self, key):
if key not in self.cache:
return -1
value = self.cache.pop(key)
self.cache[key] = value # Move to end (most recently used)
return value
def put(self, key, value):
if key in self.cache:
self.cache.pop(key)
elif len(self.cache) >= self.capacity:
self.cache.popitem(last=False) # Remove least recently used
self.cache[key] = value
# Test
cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
print(cache.get(1)) # 1
cache.put(3, 3) # Evicts key 2
print(cache.get(2)) # -119. Explain Python’s *args and **kwargs and provide an example.
Answer: *args allows a function to accept a variable number of positional arguments as a tuple, while **kwargs accepts a variable number of keyword arguments as a dictionary.
Example:
def example(a, *args, **kwargs):
print(f"a: {a}")
print(f"args: {args}")
print(f"kwargs: {kwargs}")
example(1, 2, 3, x=4, y=5)
# Output:
# a: 1
# args: (2, 3)
# kwargs: {'x': 4, 'y': 5}Coding Challenges
These practical problems are staples in technical interviews at Google, Amazon, and startups.10. Find the first non-repeating character in a string.
Answer:
def first_non_repeating_char(s):
char_count = {}
for char in s:
char_count[char] = char_count.get(char, 0) + 1
for char in s:
if char_count[char] == 1:
return char
return None
# Test
print(first_non_repeating_char("leetcode")) # Output: l11. Merge two sorted lists into one sorted list.
Answer:
def merge_sorted_lists(list1, list2):
merged = []
i, j = 0, 0
while i < len(list1) and j < len(list2):
if list1[i] <= list2[j]:
merged.append(list1[i])
i += 1
else:
merged.append(list2[j])
j += 1
merged.extend(list1[i:])
merged.extend(list2[j:])
return merged
# Test
print(merge_sorted_lists([1, 3, 5], [2, 4, 6])) # Output: [1, 2, 3, 4, 5, 6]20. Write a Python function to find the maximum subarray sum (Kadane’s Algorithm).
Answer:
def max_subarray_sum(arr):
max_so_far = arr[0]
max_ending_here = arr[0]
for num in arr[1:]:
max_ending_here = max(num, max_ending_here + num)
max_so_far = max(max_so_far, max_ending_here)
return max_so_far
# Test
print(max_subarray_sum([-2, 1, -3, 4, -1, 2, 1, -5, 4])) # 6 (subarray [4, -1, 2, 1])21. Flatten a nested list in Python.
Answer:
def flatten_list(nested):
flat = []
for item in nested:
if isinstance(item, list):
flat.extend(flatten_list(item))
else:
flat.append(item)
return flat
# Test
print(flatten_list([1, [2, 3], [4, [5, 6]]])) # [1, 2, 3, 4, 5, 6]Company-Specific Insights
- Google: Expect questions on algorithms (e.g., string manipulation, graph traversal) and Python’s internals (e.g., GIL, bytecode). Python is used in Google’s AI tools and backend systems.
- Amazon: Focus on data structures, optimization, and real-world applications like AWS automation scripts.
- Netflix: Questions often revolve around data pipelines (Pandas, NumPy) and streaming system efficiency.
- Meta: Emphasis on object-oriented programming, decorators, and large-scale system design.
- Microsoft: Deep dives into memory management, threading, and cloud integration (Azure).
Preparation Tips for 2025
- Practice Coding: Use platforms like LeetCode, HackerRank, or CodeSignal to solve Python problems.
- Master Libraries: Know Pandas, NumPy, and Flask/Django for data and web roles.
- Understand Trends: With AI and cloud computing booming in 2025, brush up on Python’s role in these areas.
- Explain Your Thought Process: Companies value clarity in how you approach problems, not just the solution.
Conclusion
Python’s reign as a top programming language in 2025 makes it a critical skill for tech interviews. Whether you’re targeting Google’s algorithmic challenges, Amazon’s data-heavy roles, or Netflix’s streaming innovations, these questions will prepare you to shine. Start practicing today, and good luck landing your dream job!Have a favorite Python question or company-specific tip? Share in the comments below!